Course Offerings — Summer Term 2026

Each MSc main module (ML 1, ML 2, DL 1, DL 2) comes in a standard variant (9 CP) and an -X variant (+3 CP) that adds one elective. CA (BSc) includes one elective. Electives only count as part of an -X module or CA, not standalone.

Which electives fit which module? (SoSe 2026 offering; electives count only as the +3 CP part of an -X module or of CA)

Recommended paths by background

• BSc, new to ML → start with CA. Pick a CA-compatible elective to complete the 6 CP module.
• MSc with solid math and programming → start with ML 1 or DL 1 in winter, continue with ML 2 / DL 2 in summer. Choose an elective to upgrade to the -X variant.
• MSc with little programming background → take PyML alongside ML 1 / DL 1, or take a CS intro first.
• MSc with little math background → take introductory math first, and plan MathML as your elective with ML 2-X / DL 2-X.
• Projects (GRP, PML, LML, Lab ML) → complete ML or DL and PyML (or equivalent) first.
• DLBSA → expect solid DL theory/practice, basic signal processing, PyTorch, and some familiarity with biomedical signals.

Our group offers modules and electives:

1. Modules:

   • Only modules count toward credits at TU Berlin.

2. Electives:
   Electives are courses or seminars offered in two formats:

   • Part of a Module:

     • Integrated into one of the following:
       • Cognitive Algorithms (CA): Must include one elective.
       • Machine Learning 1/2-X (ML 1/2-X) and Deep Learning 1/2-X (DL 1/2-X): May optionally include one elective, earning three additional CPs.
     • Passing the elective is required to take the module’s exam.
     • If graded, the elective’s grade does not affect the module’s grade.
     • A passed elective remains valid for the upcoming term.

   • Standalone:

     • Not available for students seeking credits at TU Berlin.
     • Applicable only in exceptional cases (e.g., for some exchange students).
     • An elective certificate disqualifies its use as part of a module.

This course will treat foundational topics in Machine Learning. The scheduled topics are: Low-Dimensional Embeddings (LLE, TSNE), Component Analyses (CCA, ICA), Kernel Learning (structured input, structured outputs, anomaly detection), Hidden Markov Models, Deep Learning (structured input, structured outputs, anomaly detection), Bioinformatics, Explainable AI

The scheduled topics are:

• Representation Learning
• Attention
• Density Estimation
• Generative Models
• Graph Neural Networks
• Equivariant Neural Networks
• Neural Ordinary Differential Equations
• Deep Reinforcement Learning
• Advanced Explainable AI

The course focuses on Python and applications such as Transformers, attention, and diffusion models. The exam is digital.

Introduction to the Julia programming language and its Machine Learning ecosystem. Learn how to write reproducible, unit-tested Julia code for ML research in Julia. No prior knowledge of Julia is required.

Computer programs can learn useful cognitive skills. This integrated lecture communicates an intuitive understanding of elementary concepts in machine learning and their application on real data with a special focus on methods that are simple to implement. For a more advanced treatment we recommend the “Machine Learning 1” or the “Lab Course Machine Learning” modules.

This module offers an application-driven introduction to modern deep learning methods for biomedical signal analysis. It consists of two main components: the DLBSA lecture series and the DLBSA Project. The lectures cover key topics including preprocessing techniques, core deep learning architectures (e.g., CNNs), advanced models (e.g., Transformers), and representation learning. The DLBSA Project complements the lectures by providing an end-to-end, hands-on experience in developing, evaluating, and refining deep learning models for biomedical signal data.

This is a research-oriented seminar about applications of machine learning to quantum chemistry. Students will read, understand, evaluate and present selected research papers on machine learning methods in quantum chemistry. At the end of the semester, each student will present their topic in a 20 min talk (+ 10 min questions) in English. It is possible to attend this course without prior knowledge in chemistry or physics since many papers only require a basic comprehension of the respective research topic. There is no formal registration for the kick-off meeting. In the general case, it is not possible to take the seminar as a standalone course.

Using Machine Learning for Biomedical Signal Analysis is both exciting and challenging due to its interdisciplinary nature. With a particular focus on neurotechnology and multivariate / multimodal timeseries processing, we will cover fundamentals of various biosignals such as fNIRS, EEG and ExG, techniques for pre-processing, decomposition and sensor fusion methods, feature extraction, and discuss typical challenges.

The seminar provides an introduction to academic work. Students will learn how to give a presentation about a classical topic in Machine Learning. Please note that this seminar can only be taken together with CA, DL1/2-X or ML1/2-X.

This seminar takes a closer look at a mix of hot topics in machine learning including, but not limited to: Architectures in Deep Learning, Self-Supervised Learning, Generative Models, NLP, Reinforcement Learning and Variational Inference.

The Machine Learning Colloquium is a weekly session of the Machine Learning Group for discussing ongoing research in machine learning. It is open to PhD students, postdocs, and interested Master’s students. Sessions cover a wide range of topics, including ongoing or completed projects, new research ideas, skill-sharing tutorials, thesis defenses, and invited external talks, which are presented and critically discussed. Participants are encouraged to actively contribute.

Computer programs can learn useful cognitive skills. This course takes a closer look at specific applications of machine learning algorithms. With the help of their supervisors, students read, understand, evaluate, and present selected research papers on machine learning methods in different application settings. At the end of the semester, each student presents their topic in a 15-minute talk (+ 5 minutes discussion) in English.

In this seminar, foundational and recent research in generative modelling is studied. Students will present and discuss selected papers from the field.

This is a joint, research-oriented seminar by the Machine Learning Group and the Data Management Group. Throughout the seminar, students will have the opportunity to learn about recent advances at the intersection of Machine Learning and Data Management Systems. Interested students are required to participate in the kick-off meeting, after which they will select, read, understand, and present one of the eligible papers. Moreover, the students will be required to submit a 3-slide slide deck summarizing their selected paper as a midterm examination. The final presentation, lasting 15 minutes (10 minutes presentation + 5 minutes of questions), will be held in English at the end of the semester (the exact date will be announced). Only the final presentation will be considered for the student’s final grade. More details will be discussed during the kick-off meeting. Note that as of the summer term 2024, this seminar is offered as an elective or standalone module.

In this seminar, foundational and current research in the area of explainable machine learning (XAI) is disseminated. Students may indicate their preferences and subsequently get assigned a paper to present. With the help of their supervisors, students will read, understand, evaluate, and present selected research papers on methods, applications, and theory in XAI.

Geometric Deep Learning extends deep learning to non-Euclidean structures, which might be graphs, point clouds or others. From the structure of the data naturally arise symmetries, that can be exploited to improve model performance or enhance generalization capabilities. We will study some of those methods with a special focus on graph neural networks (GNNs) that respect rotation and translation symmetries.

This course provides a series of lectures on Bayesian inferenec and generative modelling, covering the following topics: Bayesian learning, Gaussian process and Bayesian optimization, Variational inference, Sampling methods, Generative modeling.